1. Interactive Data Exploration with Databricks Notebooks


    Interactive Data Exploration with Databricks Notebooks is a powerful feature that allows data scientists and data engineers to write and execute code interactively, visualize data, and share results within their team. With Databricks Notebooks, users can work with languages such as Python, Scala, SQL, and R in a collaborative environment.

    To enable interactive data exploration with Databricks Notebooks using Pulumi, you need to set up a Databricks workspace in your cloud environment. This includes creating the workspace, setting up the necessary clusters, and creating a notebook where you can write your exploratory code.

    Below is a Pulumi program in Python that sets up a Databricks Workspace on Azure, deploys a Databricks cluster, and creates a notebook within that workspace. The program assumes you have the necessary cloud provider credentials configured.

    import pulumi import pulumi_azure as azure import pulumi_databricks as databricks # Create an Azure Resource Group for our resources resource_group = azure.core.ResourceGroup("my-resource-group") # Deploy a Databricks Workspace within the Azure Resource Group workspace = azure.databricks.Workspace("my-workspace", resource_group_name=resource_group.name, sku="standard", # Choose the Databricks SKU (standard, premium, etc.) location=resource_group.location, ) # Once the workspace is created, we can create a Databricks cluster within it. # The cluster is where the actual data processing and exploration happens. # Configure cluster settings (modify as needed) cluster_args = databricks.ClusterArgs( num_workers=2, spark_version="7.3.x-scala2.12", node_type_id="Standard_DS3_v2", ) # Create Databricks cluster cluster = databricks.Cluster("my-cluster", workspace_id=workspace.id, cluster_name="data-exploration-cluster", cluster_args=cluster_args ) # Finally, let's create a Databricks Notebook where we can write our exploration code. # Define the notebook content using the Databricks Notebook API format or import an existing notebook notebook_content = """ # Databricks notebook source # MAGIC %md # MAGIC # My Data Exploration Notebook # MAGIC This notebook will be used for interactive data exploration. """ # Create the notebook in the Databricks workspace notebook = databricks.Notebook("my-notebook", workspace_id=workspace.id, content=notebook_content, path="/Shared/MyNotebooks/DataExploration", language=databricks.NotebookLanguage.PYTHON, ) # Export the Databricks Workspace URL to easily access it from the Pulumi output pulumi.export("databricks_workspace_url", workspace.workspace_url) # Export the Notebook URL for quick access notebook_url = pulumi.Output.concat(workspace.workspace_url, "/notebook", notebook.path) pulumi.export("notebook_url", notebook_url)

    This program creates the necessary resources to start working with Databricks Notebooks:

    • It initializes an Azure Resource Group to hold the resources.
    • Then, it deploys a Databricks Workspace into the resource group.
    • It configures and deploys a cluster where the notebooks will be executed.
    • Finally, it creates a new Databricks Notebook with some boilerplate content. You can also upload existing notebooks by providing the content in the Databricks-compatible format.

    After running the program, it outputs the URLs for both the Databricks Workspace and the created Notebook, so you can access them directly. When you navigate to the provided notebook URL, you'll be taken to your Databricks Notebook within your workspace, where you can start writing and running interactive code.

    Remember that the above code is just a starting point. In a real-world scenario, you may also want to set up security, networking, and monitoring for your Databricks environment, depending on your organization's requirements.